in 3.1 (to be removed in 4.0). Convergence thresholds are
problem-dependent. As this class is intended for users who want to set
their own convergence criterion instead of relying on an algorithm's
default procedure, they should also set the thresholds appropriately
(cf. MATH-798).

Computes a cubic spline interpolation for the data set using the Akima
algorithm, as originally formulated by Hiroshi Akima in his 1970 paper
"A New Method of Interpolation and Smooth Curve Fitting Based on Local Procedures."
J.

Performs a
Chi-square goodness of fit test evaluating the null hypothesis that the
observed counts conform to the frequency distribution described by the expected
counts, with significance level alpha.

Performs a
chi-square test of independence evaluating the null hypothesis that the
classifications represented by the counts in the columns of the input 2-way table
are independent of the rows, with significance level alpha.

Represents an
empirical probability distribution -- a probability distribution derived
from observed data without making any assumptions about the functional form
of the population distribution that the data come from.

Returns true if, both for the real part and for the imaginary
part, there is no double value strictly between the arguments or the
difference between them is within the range of allowed error
(inclusive).

Returns true if, both for the real part and for the imaginary
part, there is no double value strictly between the arguments or the
relative difference between them is smaller or equal to the given
tolerance.

Evaluate method to compute the percentile for a given bounded array
using earlier computed pivots heap.
This basically calls the index and then
estimate
functions to return the estimated percentile value.